Collective Variables for the Study of Crystallization

Abstract

The phenomenon of solidification of a substance from its liquid phase is of the greatest practical and theoretical importance, and atomistic simulations can provide precious information towards its understanding and control. Unfortunately, the time scale for crystallization is much larger than what can be explored in standard simulations. Enhanced sampling methods can overcome this time scale hurdle. Here we employ the on-the-fly probability enhanced sampling method that is a recent evolution of metadynamics. This method, like many others, relies on the definition of appropriate collective variables able to capture the slow degrees of freedom. To this effect we introduce collective coordinates of general applicability to crystallization simulations. They are based on the peaks of the three-dimensional structure factor that are combined non-linearly via the Deep Linear Discriminant Analysis machine learning method. We apply the method to the study of crystallization of a multicomponent system, Sodium Chloride and a molecular system, Carbon Dioxide.

arXiv comment23 pages, 3 figures

References in corpus (22)

• Canonical sampling through velocity-rescaling
• Escaping free-energy minima
• PLUMED 2: New feathers for an old bird
• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• Accurate determination of crystal structures based on averaged local bond order parameters
• Rethinking Metadynamics: from bias potentials to probability distributions
• Data-Driven Collective Variables for Enhanced Sampling
• Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
• Rate of Homogeneous Crystal Nucleation in molten NaCl
• A Unified Approach to Enhanced Sampling
• Predicting polymorphism in molecular crystals using orientational entropy
• Enhancing entropy and enthalpy fluctuations to drive crystallization in atomistic simulations
• Neural network based path collective variables for enhanced sampling of phase transformations
• Homogeneous nucleation of ice
• High-pressure molecular phases of solid carbon dioxide
• Calculation of phase diagrams in the multithermal-multibaric ensemble
• Calculation of the melting point of alkali halides by means of computer simulations
• Finding Efficient Collective Variables: The Case of Crystallization
• Making the best of a bad situation: a multiscale approach to free energy calculation
• CO2 packing polymorphism under pressure: mechanism and thermodynamics of the I-III polymorphic transition
• Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations
• CO2 packing polymorphism under confinement in cylindrical nanopores

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• Computer Simulation of the Early Stages of Self-Assembly and Thermal Decomposition of ZIF-8
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• Deep learning the slow modes for rare events sampling
• Nucleation rates from small scale atomistic simulations and transition state theory
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